library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
## record_id = col_double(),
## month = col_double(),
## day = col_double(),
## year = col_double(),
## plot_id = col_double(),
## species_id = col_character(),
## sex = col_character(),
## hindfoot_length = col_double(),
## weight = col_double(),
## genus = col_character(),
## species = col_character(),
## taxa = col_character(),
## plot_type = col_character()
## )
survey_plot = ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length))
survey_plot + geom_point()
library(hexbin)
survey_plot + geom_hex()
compared to scatter plot, hexagonal bin plot is nicer looking but it is not granular.
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1, aes(color = species_id))
ggplot(data = surveys_complete, aes(x = species_id, y = weight)) +
geom_point(aes(color = plot_type))
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot()
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.3, color = "tomato")
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_violin(alpha = 0) +
geom_jitter(alpha = 0.3, color = "tomato") +
scale_y_log10()
surveys_complete %>%
ggplot(mapping = aes(x= species_id, y = hindfoot_length)) +
geom_jitter(alpha = 0.3, aes(color = as.factor(plot_id))) +
geom_boxplot() +
scale_y_log10()
yearly_counts <- surveys_complete %>%
count(year, genus)
ggplot(data= yearly_counts, aes(x= year, y= n)) + geom_line()
ggplot(data= yearly_counts, aes(x= year, y= n, group= genus, color= genus)) + geom_line()
yearly_counts %>%
ggplot(mapping = aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts_graph <- surveys_complete %>%
count(year, genus) %>%
ggplot(mapping = aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts_graph
ggplot(data= yearly_counts, aes(x= year, y= n)) +
geom_line() +
facet_wrap(facets = vars(genus))
year_sex_count <- surveys_complete %>%
count(year, genus, sex)
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_wrap(facets = vars(genus))
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_grid(rows = vars(sex), cols = vars(genus))
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_grid(rows = vars(genus))
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_grid(cols = vars(genus))
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_wrap(facets = vars(genus)) +
theme_bw()
avg_weight_yearly <- surveys_complete %>%
group_by(year, species_id) %>%
summarize(avg_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data= avg_weight_yearly, mapping = aes(x= year, y= avg_weight)) +
geom_line() +
facet_wrap(facets = vars(species_id)) +
theme_bw()
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_wrap(facets = vars(genus)) +
theme_bw() +
labs(title= "Observed genera through time",
x= "Years of observation",
y= "Numbers of individuals")
ggplot(data= year_sex_count, mapping = aes(x= year, y= n, color= sex)) +
geom_line() +
facet_wrap(facets = vars(genus)) +
theme_bw() +
labs(title= "Observed genera through time",
x= "Years of observation",
y= "Numbers of individuals") +
theme(text = element_text(size = 15))
ggplot(data = year_sex_count, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(colour = "grey20", size = 6), strip.text = element_text(face = "italic"), text = element_text(size = 16))
grey_theme <- theme(axis.title.x = element_text(colour="grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5), axis.title.y = element_text(colour= "grey20", size= 12), text = element_text(size = 16))
ggplot(surveys_complete, aes(x= species_id, y= hindfoot_length)) +
geom_boxplot() +
grey_theme
grey_theme <- theme(axis.title.x = element_text(colour="grey20", size = 12, hjust = 0.5, vjust = 0.5), axis.title.y = element_text(colour= "grey20", size= 12), text = element_text(size = 16))
ggplot(surveys_complete, aes(x= species_id, y= weight, color= sex)) +
geom_boxplot() +
grey_theme +
labs(title= "Boxplot of Weight vs species_id", x="Species ID", y= "Weights")